PROJECT SUMMARY ? PROJECT 2 (AIM 5) Measuring and modeling the tumor and immune microenvironment before and after therapy. The overall goal of Project 2 is to determine which features of a tumor and its microenvironment make it responsive to ICIs or targeted therapies alone or in combination. We will collect quantitative data at single cell resolution on the identities, states and physical arrangement of tumor, stromal and immune cells and on soluble and ECM components that comprise the tumor microenvironment (TME). This will be accomplished using highly multiplexed fluorescence imaging of standard formalin fixed, paraffin-embedded (FFPE) tissue and tumor samples combined with single cell RNA sequencing (scRNA). To provide insight into causal relationships among variables, we will analyze samples collected at different points in time, most commonly biopsies prior to and on therapy, and at the time of drug-resistant disease. In the case of ICI-induced skin toxicities, we will perform localized interventional studies (e.g. treatment with retinoids) followed by biopsies to determine the effectiveness of treatment and to test specific hypotheses about immune cell homeostasis in skin, respectively. Much of the work in this project will be hypothesis generating and will be tightly integrated with hypothesis testing studies in cells and mice in Projects 1 and 3. Aim 5.1 will focus on experimental and computational methods for obtaining 20-60 channel images from formalin-fixed, paraffin embedded (FFPE) tissue and tumor samples using tissue-based cyclic immunofluorescence (t-CycIF). Aim 5.2 will integrate high dimensional t-CycIF imaging and single cell RNA sequencing to generate data on the composition and states of tumor, stromal and immune cells at single-cell resolution (?deep tumor phenotypes?). Aim 5.3 will use deep phenotyping to analyze tumors from BRAFV600E patients treated with BRAF and MEK inhibitors or patients treated with ICIs irrespective of the BRAF mutation status. Biopsies collected before and during therapy, and at the time of progression, will be used to identify changes in the malignant cells, TME and immune cell cohort associated with, and potentially predictive of, therapeutic response and drug resistance. Aim 5.4 will analyze the effects of ICIs on skin-resident T-cells and compare adverse responses to the idiopathic conditions they resemble; analysis of local responses to retinoids and steroids will provide new insight into immune homeostasis in the skin. Aim 5.5 will identify features associated with (and ultimately predictive of) exceptional response to ICIs in brain tumors and provide data on biomarkers that can be evaluated in Bayesian adaptive clinical trials. Aim 5.6 will investigate the connection between immune infiltration and intrinsic or drug-induced genomic instability in triple negative breast cancers (TNBC). Aim 5.7 will integrate data on tumor phenotypes, drug interventions and clinical responses using a range of supervised and unsupervised machine-learning methods, including methods based on network priors, and also link scRNA transcriptomics with t-CycIF image data using a multi-view learning framework.